The pipeline of our network is shown in Fig. REF  where we utilize a simple feature-space adversarial network as the backbone. To purify the encoded latent representation, we adopt an information constraint on the latent space, encouraging \(F\)  to encode only task-dependent semantic features into the representations. Built upon the recently developed information theories for deep learning [1]}, [2]},
we achieve such constraint by employing a variational information bottleneck into the feature extractor \(F\) , which is shared among the source domain and target domain respectively. In this case, we obtain the following objective function:
